This study focuses on a CFD modelling of biomass-derived syngas co-firing with coal in an older mid-sized PC-fired boiler of type OP-230 with low-emission burners on the front wall. The simulations were performed to d...This study focuses on a CFD modelling of biomass-derived syngas co-firing with coal in an older mid-sized PC-fired boiler of type OP-230 with low-emission burners on the front wall. The simulations were performed to determine whether the boiler can be retrofitted for the fulfilment of the prospective environmental protection regulations relating to levels of NO_X emissions. The improvement of the air staging via the dual-fuel technique was based on the indirect co-firing technology. The impact of two arrangements of dedicated syngas nozzles(below and above the existing coal burners), two syngas compositions and two heat replacements(5% and 15%) on the course of thermal processes in a furnace was tested. The reductions in NO_X emissions were predicted relative to the baseline when only coal is combusted. The highest reduction of about 38% was achieved with the syngas nozzles below the existing coal burners and 15% heat replacement. This arrangement of nozzles offers the residence time sufficient to co-fire coal with waste derived syngas. A lower reduction in NO_X emissions was obtained with the nozzles above the burners as the enlargement of local fuel rich zone near syngas injection becomes significant for 15% heat replacement. Results provide for the decreasing impact of methane content along with the increase of syngas heat input. The avoided CO_2 emissions through the syngas indirect co-firing with coal in the boiler are linear function of heat replacements.展开更多
One of the most critical factors affecting boiler efficiency and hazardous-gas-emission reduction is the volume of excess air mixed with fuel.A knowledge-based approach is proposed to model the efficiency of a 320-MW ...One of the most critical factors affecting boiler efficiency and hazardous-gas-emission reduction is the volume of excess air mixed with fuel.A knowledge-based approach is proposed to model the efficiency of a 320-MW natural-gas-fired steam power plant in Isfahan,Iran by applying fuzzy-modelling techniques to control the boiler efficiency.This model is based on fuel and air entering the boiler.First,the fuzzy-model structure is identified by applying the fuzzy rules obtained from an experienced human operator.The proposed method is then optimized using a genetic algorithm to increase the fuzzy-model accuracy.The results indicate that,by applying a genetic algorithm,the precision of the proposed fuzzy model increases.The error between the actual efficiency of the plant and the output efficiency of the proposed model is low.This model is developed by applying the fuzzy rules and modelling-related calculations.Finally,to optimize the efficiency of the boiler,a fuzzy proportional-integral controller is designed.The closed-loop control simulations are run by applying both the proposed controller and the manual controller to demonstrate the influence of the suggested method.The simulation outcomes indicate that the recommended controller adjusts the excess-air percentage correctly and increases the unit efficiency by 0.70%,significantly reducing fuel consumption.展开更多
Boilers are significant contributors to carbon emissions and pollutants across various industrial sectors.Accurately modeling furnace temperature is critical for optimizing combustion and enhancing operational efficie...Boilers are significant contributors to carbon emissions and pollutants across various industrial sectors.Accurately modeling furnace temperature is critical for optimizing combustion and enhancing operational efficiency.However,modeling poses significant challenges due to the interplay between rapidly changing dynamic processes and slowly varying static data.To address the coupling and redundancy inherent in these heterogeneous features,a hybrid framework for boiler temperature modeling(HFBTM)is proposed in this paper.The framework utilizes a multi-layer dense network to extract static features and a selective state space model(S3M)to capture dynamic features.These features are effectively combined through a hybrid feature fusion module using weighted integration,generating accurate temperature predictions across multiple future time steps.Compared with traditional single dynamic models,HFBTM mitigates information redundancy,reduces error propagation,and serves as an end-to-end furnace temperature prediction model that integrates both static and dynamic features.Experimental results demonstrate that the HFBTM framework delivers superior prediction performance across forecasting tasks of varying lengths.Compared with the existing methods,the proposed framework achieves higher accuracy and meets the requirements for precise modeling of boiler systems.展开更多
基金carried out in the framework of 3190/23/P and S/WZ/1/2015 works financed by Ministry of Science and Higher Education of Poland from the funds for science
文摘This study focuses on a CFD modelling of biomass-derived syngas co-firing with coal in an older mid-sized PC-fired boiler of type OP-230 with low-emission burners on the front wall. The simulations were performed to determine whether the boiler can be retrofitted for the fulfilment of the prospective environmental protection regulations relating to levels of NO_X emissions. The improvement of the air staging via the dual-fuel technique was based on the indirect co-firing technology. The impact of two arrangements of dedicated syngas nozzles(below and above the existing coal burners), two syngas compositions and two heat replacements(5% and 15%) on the course of thermal processes in a furnace was tested. The reductions in NO_X emissions were predicted relative to the baseline when only coal is combusted. The highest reduction of about 38% was achieved with the syngas nozzles below the existing coal burners and 15% heat replacement. This arrangement of nozzles offers the residence time sufficient to co-fire coal with waste derived syngas. A lower reduction in NO_X emissions was obtained with the nozzles above the burners as the enlargement of local fuel rich zone near syngas injection becomes significant for 15% heat replacement. Results provide for the decreasing impact of methane content along with the increase of syngas heat input. The avoided CO_2 emissions through the syngas indirect co-firing with coal in the boiler are linear function of heat replacements.
文摘One of the most critical factors affecting boiler efficiency and hazardous-gas-emission reduction is the volume of excess air mixed with fuel.A knowledge-based approach is proposed to model the efficiency of a 320-MW natural-gas-fired steam power plant in Isfahan,Iran by applying fuzzy-modelling techniques to control the boiler efficiency.This model is based on fuel and air entering the boiler.First,the fuzzy-model structure is identified by applying the fuzzy rules obtained from an experienced human operator.The proposed method is then optimized using a genetic algorithm to increase the fuzzy-model accuracy.The results indicate that,by applying a genetic algorithm,the precision of the proposed fuzzy model increases.The error between the actual efficiency of the plant and the output efficiency of the proposed model is low.This model is developed by applying the fuzzy rules and modelling-related calculations.Finally,to optimize the efficiency of the boiler,a fuzzy proportional-integral controller is designed.The closed-loop control simulations are run by applying both the proposed controller and the manual controller to demonstrate the influence of the suggested method.The simulation outcomes indicate that the recommended controller adjusts the excess-air percentage correctly and increases the unit efficiency by 0.70%,significantly reducing fuel consumption.
基金supported by the National Natural Science Foundation of China(No.61833011).
文摘Boilers are significant contributors to carbon emissions and pollutants across various industrial sectors.Accurately modeling furnace temperature is critical for optimizing combustion and enhancing operational efficiency.However,modeling poses significant challenges due to the interplay between rapidly changing dynamic processes and slowly varying static data.To address the coupling and redundancy inherent in these heterogeneous features,a hybrid framework for boiler temperature modeling(HFBTM)is proposed in this paper.The framework utilizes a multi-layer dense network to extract static features and a selective state space model(S3M)to capture dynamic features.These features are effectively combined through a hybrid feature fusion module using weighted integration,generating accurate temperature predictions across multiple future time steps.Compared with traditional single dynamic models,HFBTM mitigates information redundancy,reduces error propagation,and serves as an end-to-end furnace temperature prediction model that integrates both static and dynamic features.Experimental results demonstrate that the HFBTM framework delivers superior prediction performance across forecasting tasks of varying lengths.Compared with the existing methods,the proposed framework achieves higher accuracy and meets the requirements for precise modeling of boiler systems.
